Methods and apparatuses for using artificial intelligence trained to generate candidate drug compounds based on dialects

Abstract

In one aspect, a method is disclosed for using dialects to generate candidate drug compounds. The dialects describe sequences of the candidate drug compounds and activities associated with the sequences. The method includes receiving a data set, training, using the data set, first layers of a machine learning model to determine relationships of components of a portion of a string described by a first dialect. The components pertain to amino acids associated with first activity level information of the sequences. The method includes training, using the data set and the portion of the string, a final layer to generate a remainder of the string. The remainder pertains to second activity level information of the sequences. The method includes generating, using the first and final layer, the string comprising the portion and the remainder. The string represents a candidate drug compound.

1 - 11 . (canceled) 
     
     
         12 . A tangible, non-transitory computer-readable medium storing instructions that, when executed, cause a processing device to:
 generate, via dialects, one or more candidate drug compounds, wherein the dialects describe one or more sequences of the one or more candidate drug compounds and activities associated with the one or more sequences of the candidate drug compounds, and wherein executing the instructions further causes the processing device to:
 receive a data set comprising a network of biological context representations; 
 train, using the data set, one or more first layers of a machine learning model to determine relationships of one or more components of a portion of a string described by a first dialect, wherein the one or more components pertain to amino acids associated with first activity level information of the one or more sequences, and the one or more first layers comprise one or more nodes executing one or more objective functions that optimize at least one secondary objective related to the first activity level information; 
 train, using logical rules of the first dialect and the portion of the string, a final layer of the machine learning model to generate a remainder of the string, wherein:
 the remainder of the string pertains to second activity level information of the one or more sequences, 
 the final layer comprises one or more nodes executing one or more objective functions that optimize at least one primary objective related to the second activity level information, 
 the logical rules define a semantic meaning based on lexical elements associated with the string, and 
 the semantic meaning specifies an order by which to encode the string to provide the first and second activity levels; and 
 
 generate, using the one or more first layers and the final layer, the string comprising the portion and the remainder by arranging, according to the logical rules, a sequence of amino acids included in the string, wherein the string represents a first candidate drug compound comprising the sequence of amino acids associated with the first activity level information and the second activity level information; and 
   synthesize, via at least a reaction chamber of an automated flow synthesis platform, the first candidate drug compound in order to create a drug compound.   
     
     
         13 . The computer-readable medium of  claim 12 , wherein the processing device is further to:
 receive input to generate a second candidate drug compound, wherein the input is based on third activity level information associated with a second dialect;   train, using logical rules of the second dialect and the portion of the string, a second final layer of the machine learning model to generate a second remainder of the string, wherein the second remainder of the string pertains to the third activity level information of the one or more sequences;   replace the final layer of the machine learning model with the second final layer; and   generate, using the one or more first layers and the second final layer and by arranging a sequence of amino acids included in the string according to the logical rules of the second dialect, a second string comprising the portion and the second remainder, wherein the second string represents a second candidate drug compound comprising the amino acids associated with the first activity level information and the third activity level information.   
     
     
         14 . The computer-readable medium of  claim 13 , wherein the first candidate drug compound is associated with a first dialect and the second candidate drug compound is associated with a second dialect. 
     
     
         15 . The computer-readable medium of  claim 14 , wherein the first and second dialects pertain to peptide sequences that have different activity levels. 
     
     
         16 . The computer-readable medium of  claim 13 , wherein the machine learning model uses the portion of the string as determined by the one or more first layers of the machine learning model to determine the relationships of the one or more components of the portion of the string and determines the second remainder by inputting the portion of the string into the second final layer. 
     
     
         17 . The computer-readable medium of  claim 12 , wherein the network of biological context representations comprises:
 a plurality of information pertaining to structural drug information, semantic drug information, drug activity level information, drug biomedical information, drug physiochemical information, pharmacokinetic drug information, pharmacodynamic drug information, pharmacogenetic drug information, or some combination thereof, and   characterizations of relationships between the plurality of information.   
     
     
         18 . The computer-readable medium of  claim 12 , wherein, based on a primary objective function to be optimized by the machine learning model, the final layer generates the first candidate drug compound. 
     
     
         19 . The computer-readable medium of  claim 18 , wherein the primary objective function comprises a type of activity level capable of being provided by the candidate drug compound, wherein the type of activity level comprises anti-infective, anti-cancer, antimicrobial, anti-viral, anti-fungal, anti-inflammatory, anti-cholinergic, anti-dopaminergic, anti-serotonergic, anti-noradrenergic, anti-prionic, anti-fungal functional biomaterials, or some combination thereof. 
     
     
         20 . A system comprising:
 a computer-readable medium storing instructions;   a processing device communicatively coupled to the computer-readable medium, wherein the processing device executes the instructions to:
 generate, via dialects, one or more candidate drug compounds, wherein the dialects describe one or more sequences of the one or more candidate drug compounds and activities associated with the one or more sequences of the one or more candidate drug compounds, and wherein the processing device generates the one or more candidate drug compounds by:
 receiving a data set comprising a network of biological context representations; 
 training, using the data set, one or more first layers of a machine learning model to determine relationships of one or more components of a portion of a string described by a first dialect, wherein the one or more components pertain to amino acids associated with first activity level information of the one or more sequences, and the one or more first layers comprise one or more nodes executing one or more objective functions that optimize at least one secondary objective related to the first activity level information, and; 
 training, using logical rules of the first dialect and the portion of the string, a final layer of the machine learning model to generate a remainder of the string, wherein:
 the remainder of the string pertains to second activity level information of the one or more sequences, 
 the final layer comprises one or more nodes executing one or more objective functions that optimize at least one primary objective related to the second activity level information, 
 the logical rules define a semantic meaning based on lexical elements associated with the string, and 
 the logical rules pertain to an order by which to encode the string to provide the first and second activity levels; and 
 
 generating, using the one or more first layers and the final layer, the string comprising the portion and the remainder by arranging, according to the logical rules, a sequence of amino acids included in the string, wherein the string represents a first candidate drug compound comprising the sequence of amino acids associated with the first activity level information and the second activity level information; and 
 
 synthesize, via at least a reaction chamber of an automated flow synthesis platform, the first candidate drug compound in order to create a drug compound. 
   
     
     
         21 . The system of  claim 20 , wherein the processing device is further to:
 receive input to generate a second candidate drug compound, wherein the input is based on third activity level information associated with a second dialect;   train, using logical rules of the second dialect and the portion of the string, a second final layer of the machine learning model to generate a second remainder of the string, wherein the second remainder of the string pertains to the third activity level information of the one or more sequences;   replace the final layer of the machine learning model with the second final layer; and   generate, using the one or more first layers and the second final layer and by arranging a sequence of amino acids included in the string according to the logical rules of the second dialect, a second string comprising the portion and the second remainder, wherein the second string represents a second candidate drug compound comprising the amino acids associated with the first activity level information and the third activity level information.   
     
     
         22 . The system of  claim 21 , wherein the first candidate drug compound is associated with a first dialect and the second candidate drug compound is associated with a second dialect. 
     
     
         23 . The system of  claim 22 , wherein the first and second dialects pertain to peptide sequences that have different activity levels. 
     
     
         24 . The system of  claim 21 , wherein the machine learning model uses the portion of the string as determined by the one or more first layers of the machine learning model to determine the relationships of the one or more components of the portion of the string and determines the second remainder by inputting the portion of the string into the second final layer. 
     
     
         25 . The system of  claim 20 , wherein the network of biological context representations comprises:
 a plurality of information pertaining to structural drug information, semantic drug information, drug activity level information, drug biomedical information, drug physiochemical information, pharmacokinetic drug information, pharmacodynamic drug information, pharmacogenetic drug information, or some combination thereof, and   characterizations of relationships between the plurality of information.   
     
     
         26 . The system of  claim 20 , wherein, based on a primary objective function to be optimized by the machine learning model, the final layer generates the first candidate drug compound. 
     
     
         27 . The system of  claim 26 , wherein the primary objective function comprises a type of activity level capable of being provided by the candidate drug compound, wherein the type of activity level comprises anti-infective, anti-cancer, antimicrobial, anti-viral, anti-fungal, anti-inflammatory, anti-cholinergic, anti-dopaminergic, anti-serotonergic, anti-noradrenergic, anti-prionic, anti-fungal functional biomaterials, or some combination thereof. 
     
     
         28 . An apparatus comprising:
 a computer-readable medium storing instructions;   a processing device communicatively coupled to the computer-readable medium, wherein the processing device executes the instructions to:
 generate, via dialects, one or more candidate drug compounds, wherein the dialects describe one or more sequences of the one or more candidate drug compounds and activities associated with the one or more sequences of the one or more candidate drug compounds, and wherein the processing device generates the one or more candidate drug compounds by:
 receive a data set comprising a network of biological context representations; 
 train, using the data set, one or more first layers of a machine learning model to determine relationships of one or more components of a portion of a string described by a first dialect, wherein the one or more components pertain to amino acids associated with first activity level information of the one or more sequences, and the one or more first layers comprise one or more nodes executing one or more objective functions that optimize at least one secondary objective related to the first activity level information; 
 train, using logical rules of the first dialect and the portion of the string, a final layer of the machine learning model to generate a remainder of the string, wherein:
 the remainder of the string pertains to second activity level information of the one or more sequences, 
 the final layer comprises one or more nodes executing one or more objective functions that optimize at least one primary objective related to the second activity level information, 
 the logical rules define a semantic meaning based on lexical elements associated with the string, and 
 the semantic meaning specifies an order by which to encode the string to provide the first and second activity levels; and 
 
 generate, using the one or more first layers and the final layer, the string comprising the portion and the remainder by arranging, according to the logical rules, a sequence of amino acids included in the string, wherein the string represents a first candidate drug compound comprising the sequence of amino acids associated with the first activity level information and the second activity level information; and 
 
 synthesize, via at least a reaction chamber of an automated flow synthesis platform, the first candidate drug compound to create a drug compound. 
   
     
     
         29 . The apparatus of  claim 28 , wherein the processing device is further to:
 receive input to generate a second candidate drug compound, wherein the input is based on third activity level information associated with a second dialect;   train, using logical rules of the second dialect and the portion of the string, a second final layer of the machine learning model to generate a second remainder of the string, wherein the second remainder of the string pertains to the third activity level information of the one or more sequences;   replace the final layer of the machine learning model with the second final layer; and   generate, using the one or more first layers and the second final layer and by arranging a sequence of amino acids included in the string according to the logical rules of the second dialect, a second string comprising the portion and the second remainder, wherein the second string represents a second candidate drug compound comprising the amino acids associated with the first activity level information and the third activity level information.   
     
     
         30 . The apparatus of  claim 29 , wherein the first candidate drug compound is associated with a first dialect and the second candidate drug compound is associated with a second dialect.